Exploring Quantum Neural Networks for Demand Forecasting

TL;DR

This paper investigates the use of quantum neural networks for demand forecasting, demonstrating comparable accuracy to classical models but with fewer parameters and faster convergence, highlighting quantum computing's potential in predictive analytics.

Contribution

It introduces a quantum neural network approach for demand prediction and compares its performance to classical models, showing efficiency gains and similar predictive capacity.

Findings

Quantum neural networks achieve similar accuracy to classical models.

Quantum models require fewer training parameters.

Quantum models converge faster during training.

Abstract

Forecasting demand for assets and services can be addressed in various markets, providing a competitive advantage when the predictive models used demonstrate high accuracy. However, the training of machine learning models incurs high computational costs, which may limit the training of prediction models based on available computational capacity. In this context, this paper presents an approach for training demand prediction models using quantum neural networks. For this purpose, a quantum neural network was used to forecast demand for vehicle financing. A classical recurrent neural network was used to compare the results, and they show a similar predictive capacity between the classical and quantum models, with the advantage of using a lower number of training parameters and also converging in fewer steps. Utilizing quantum computing techniques offers a promising solution to overcome the limitations of traditional machine learning approaches in training predictive models for complex market dynamics.